CONTEMPORARY AGRICULTURE FACES SEVERAL NEW CHALLENGES IMPOSED BY ENVIRONMENTAL FACTORS SUCH AS SCARCITY OF ARABLE LAND AND CLIMATE CHANGE. ACCELERATED CROP IMPROVEMENTS AND SIGNIFICANT ADVANCEMENTS IN UNDERSTANDING OF THE PLANT'S RESPONSE TO STRESSES ARE NEEDED TO MEET THE GLOBAL FOOD DEMAND AND TO COPE WITH THE PREDICTED DRAMATIC CHANGES IN CLIMATE CONDITIONS AND TO ENSURE ENVIRONMENTAL SUSTAINABILITY. REPEATED AND QUICK MEASUREMENT OF CROP PHENOTYPIC PARAMETERS IS A MAJOR BOTTLENECK IN PLANT BREEDING PROGRAMS AND FOR CLOSING THE GAP BETWEEN GENOMICS DATA AND PHENOTYPE, AND HIGH-THROUGHPUT PHENOTYPING (HTP) TECHNOLOGIES HAVE BEEN PROPOSED TO ADDRESS THIS ISSUE. AUTOMATED DATA COLLECTION FOR HTP AND DETAILED DATA MANAGEMENT AND PROCESSING HAVE RECENTLY PROVEN THEIR BENEFITS IN OBTAINING PHENOTYPIC INFORMATION. HOWEVER, IN EXISTING PLATFORMS, DATA COLLECTED FROM THE FIELD ARE STORED LOCALLY AND LATER TRANSFERRED AND PROCESSED OFFLINE. TO TAP THE FULL POTENTIALS OF PHENOTYPING, MULTI-SCALE VARIETY (PLOT/PLANT/FIELD), HETEROGENEOUS AND LARGE VOLUME DATA SHOULD BE COLLECTED BY VARIOUS STATIC AND MOBILE SENSORS (E.G., ROBOTIC DEVICES) CONNECTED THROUGH INTERNET OF THINGS (IOT) TECHNOLOGY. ALTHOUGH LARGE DATASETS CAN BE USEFUL FOR PHENOTYPING, THEY RAISE SEVERAL CHALLENGES, E.G., COMBINING DATA FROM VARIOUS SENSORS/SOURCES, PROVENANCE, CONTEXTUALIZATION, DATA MANAGEMENT, STORAGE, EXTRACTING FEATURES AND VISUALIZATION, WHICH OVERALL MAKE IT A BIG DATA PROBLEM. IT IS THEN CRITICAL TO DEVELOP A NEW PLATFORM TO COLLECT DATA, HANDLE REAL-TIME STREAMS, ANALYZE AND MANAGE SUCH LARGE DATASETS FOR HTP APPLICATIONS, WHICH THIS RESEARCH INTENDS TO DO. THE PROPOSED RESEARCH IS IMPORTANT TO THE GENERAL PUBLIC BECAUSE IT WILL: (1) SATISFY AND ENHANCE HUMAN FOOD AND FIBER NEEDS BY IMPROVING CROP YIELD THROUGH THE EFFICIENT USE OF HTP TECHNOLOGY; AND (2) SUSTAIN THE ECONOMIC VIABILITY OF FARM OPERATIONS. THE EFFICIENT AND HIGH PERFORMANCE IOT-ENABLED, BIG DATA CYBER-PHYSICAL SYSTEMOF THIS RESEARCH SUITS AGRICULTURE AND FOOD INDUSTRY NEEDS.TO MEET SUCH UNPRECEDENTED NEEDS, AIMING AT IMPROVING CROP QUALITY AND YIELD, THIS PROJECT OFFERS A COMPELLING RESEARCH PLAN TO BUILD AN AUTONOMOUS PLATFORM UTILIZING SMART SENSORS AND ROBOTIC DEVICES CONNECTED THROUGH IOT TECHNOLOGY, AS WELL AS A SUITE OF NEW ANALYTICAL TOOLS TO COLLECT, MANAGE AND ANALYZE LARGE DATASETS IN ORDER TO STUDY THE MORPHOLOGICAL, PHYSIOLOGICAL AND PATHOLOGICAL TRAITS WITHOUT CAUSING DAMAGE TO THE PLANTS. THIS DATA CAN BE POTENTIALLY USED IN COMBINATION WITH ENVIRONMENTAL AND GENOTYPIC DATA TO MAKE BREEDING DECISIONS, TO UNCOVER RELATIONSHIPS BETWEEN GENOTYPES AND PHENOTYPES AND FOR AUTOMATED MONITORING OF PLANT HEALTH STATUS TO REDUCE QUALITATIVE AND QUANTITATIVE LOSSES DURING CROP PRODUCTION. THE ULTIMATE GOALS OF THE PROJECT WILL BE IN: (1) FACILITATING REAL-TIME DECISION MAKING FOR AN IMPROVED FIELD-BASED PLANTS PHENOTYPING; AND (2) DEVELOPING OPEN-SOURCE DATA ANALYTIC PLATFORMS TO IMPROVE AFFORDABILITY, PENETRATION AND ADOPTION OF AI TECHNOLOGIES AMONG THE STAKEHOLDERS, AND MOST IMPORTANTLY FARMERS (RESULTING IN SOCIETAL BENEFITS). IF THOSE GOALS ARE MET, THE GENERAL IMPACT WOULD BE TO INSPIRE HOW THE INTERSECTION OF BIG DATA ANALYTICS AND IOT-ENABLED DATABASES CAN TRANSFORM FARM OPERATIONS AND FARM MANAGEMENT, AS WELL AS HTP. IT WILL ALSO OPEN UP NEW AVENUES TO UTILIZE NOVEL (AND EMERGING) DATA-DRIVEN APPROACHES IN AGRICULTURAL PROCESSES. ANOTHER SIGNIFICANT IMPACT OF THIS PROJECT IS THE CAPABILITY IT WILL CREATE TO SHARE CURATED AND LABELED PHENOTYPIC DATA WITH THE SCIENTIFIC COMMUNITY.